GVPT Maths Camp

Learning objectives for today

1. Introduction to R

2. Create your first plot in R

3. Test your hypotheses using informative data visualizations

The Research Process

Source: R4DS

Data visualization

• A great way to get to know your data

• Critical to communicate your findings

R basics

R code:

1 + 2

[1] 3

Functions:

sum(1, 2)

[1] 3

EXERCISE

1. Open up RStudio.
2. Using the console, find the summation of 45, 978, and 121.
3. What is 67 divided by 6?
4. What is the square root of 894?

CHECK YOUR ANSWERS

Using the consol, find the summation of 45, 978, and 121.

sum(45, 978, 121)

[1] 1144

Or:

45 + 978 + 121

[1] 1144

What is 67 divided by 6?

67 / 6

[1] 11.16667

What is the square root of 894?

sqrt(894)

[1] 29.89983

R packages

Packages are collections of R functions, data, and compiled code in a well-defined format.

# Install the relevant package(s)
install.packages("tidyverse")

# Load the packages in current session
library(tidyverse)

EXERCISE

1. Open up RStudio.
2. Using the consol, install the tidyverse packages.

install.packages("tidyverse")

3. Load these packages in your current session

library(tidyverse)

RStudio Projects

For your sanity’s sake, for your co-author’s sanity’s sake

Keeps everything:

• Organised

• Reproducible

• Sustainable

EXERCISE

1. In the RStudio console, type in getwd() to see where you are on your computer.

EXERCISE

2. Create a new RStudio project for this Maths Camp:

Source: R4DS

EXERCISE

2. Create a new RStudio project for this Maths Camp:

Source: R4DS

EXERCISE

2. Create a new RStudio project for this Maths Camp:

Source: R4DS

Data visualisation

From R4DS - Data Visualization:

Do cars with big engines use more fuel than cars with small engines?

R4DS

This session will borrow (read: steal) heavily from Hadley Wickham’s R for Data Science book.

Skipping to the end

How did we do this?

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(colour = class, shape = class)) + 
  geom_smooth(method = "lm") + 
  theme(
    legend.position = "bottom",
    panel.grid = element_blank(),
    panel.background = element_blank(),
    plot.title.position = "plot",
    plot.title = element_text(face = "bold")
  ) + 
  labs(
    title = "Engine displacement and highway miles per gallon",
    subtitle = "Values for seven different classes of cars",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon"
  ) + 
  scale_color_colorblind()

Load relevant packages and data

# Load the relevant packages
library(tidyverse)

# Load the data
mpg

EXERCISE

Learn more about this data set by typing ?mpg into your console.

The mpg data set

glimpse(mpg)

Rows: 234
Columns: 11
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "…
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
$ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.…
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…
$ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, …
$ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto…
$ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4…
$ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1…
$ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2…
$ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p…
$ class        <chr> "compact", "compact", "compact", "compact", "compact", "c…

The mpg data set

A couple of useful variables:

• displ: engine displacement, in litres

• hwy: highway miles per gallon

Set up your plot

An empty canvas!

library(ggplot2)
library(ggthemes)

ggplot(data = mpg)

Map your aesthetics

ggplot(data = mpg, mapping = aes(x = displ, y = hwy))

Add in your cars

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point()

Let’s look at groups in the data

• We are not restricted to looking at only two interesting elements of our data.

• You can use visual elements or aesthetics (aes) to communicate many dimensions in your data.

• Let’s look at a categorical variable: the class of car (SUV, 2 seater, pick up truck, etc.).

• Look for meaningfully defined groups.

Let’s look at groups in the data

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = class)) + 
  geom_point()

Make your visualizations more accessible

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = class, shape = class)) + 
  geom_point() + 
  scale_color_colorblind()

Add more information

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = class, shape = class)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  scale_color_colorblind()

Look at the relationship across all cars

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(colour = class, shape = class)) + 
  geom_smooth(method = "lm") + 
  scale_color_colorblind()

Add useful titles and labels

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(colour = class, shape = class)) + 
  geom_smooth(method = "lm") + 
  labs(
    title = "Engine displacement and highway miles per gallon",
    subtitle = "Values for seven different classes of cars",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon"
  ) + 
  scale_color_colorblind()

Add useful titles and labels

EXERCISE

How many rows are in mpg? How many columns?

nrow(mpg)
ncol(mpg)

What does the drv variable describe?

?mpg

EXERCISE

Make a scatterplot of hwy vs cyl.

What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

Which geom might be a better choice?

EXERCISE

Why does the following give an error and how would you fix it?

ggplot(data = mpg) + 
  geom_point()

Add the following caption to the plot you made in the previous exercise: “Data come from the ggplot2 package.” HINT: Look at the documentation for labs().

Flexible visualization

You can use visual elements to communicate your findings in engaging ways.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class == "2seater"))

Changing the look of your plots

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), colour = "red")

EXERCISE

What’s gone wrong with this code? Why are the points not blue?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

EXERCISE

Which variables in mpg are categorical? Which variables are continuous?

Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

What happens if you map the same variable to multiple aesthetics?

Let’s clean our graph up

Less is more when it comes to data visualization.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(colour = class, shape = class)) + 
  geom_smooth(method = "lm") + 
  theme_minimal() + 
  labs(
    title = "Engine displacement and highway miles per gallon",
    subtitle = "Values for seven different classes of cars",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon"
  ) + 
  scale_color_colorblind()

Let’s clean this up

Creating your own theme

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(colour = class, shape = class)) + 
  geom_smooth(method = "lm") + 
  theme(
    legend.position = "bottom",
    panel.grid = element_blank(),
    panel.background = element_blank(),
    plot.title.position = "plot",
    plot.title = element_text(face = "bold")
  ) + 
  labs(
    title = "Engine displacement and highway miles per gallon",
    subtitle = "Values for seven different classes of cars",
    x = "Engine displacement (L)",
    y = "Highway miles per gallon"
  ) + 
  scale_color_colorblind()

Creating your own theme

EXERCISE

1. Customize the last plot you made using the theme() argument.

Visualizing distributions

ggplot(mpg, aes(x = drv)) + 
  geom_bar()

Visualizing distributions

Reorder in relation to frequency

ggplot(mpg, aes(x = fct_infreq(drv))) +
  geom_bar()

Visualizing numeric variables

ggplot(mpg, aes(x = hwy)) +
  geom_histogram()

Visualizing numeric variables

ggplot(mpg, aes(x = hwy)) +
  geom_density()

Visualizing numeric variables

ggplot(mpg, aes(x = hwy, colour = drv)) +
  geom_density()

Visualizing numeric variables

ggplot(mpg, aes(x = hwy, colour = drv, fill = drv)) +
  geom_density(alpha = 0.5)

Summary

This session you:

1. Set up your data science tools

2. Plotted complex data in an engaging way

3. Discovered interesting relationships in the data

4. Connected these relationships or trends to your expectations (or hypotheses about the data)

HOMEWORK

In the final session, you will apply the skills you will learn over the next few days to a problem that interests you. To prepare for this, you need to find a data set that:

1. Is relevant to your research interests,

2. Contains continuous and discrete variables.